Proper Size Research Articles

Graphene oxide-manganese oxide composite as an electrocatalyst for simultaneous detection of manganese- and chromium-contaminated water.

The development of asensitive and selective electrochemical sensor for simultaneous quantification of manganese (Mn(II)) and chromium (Cr(VI)) using composite of graphene oxide(GO) and manganese oxide modified screen printed carbon electrode (GO-Mn2O3/SPCE) is reported for the first time. The good sensing performance is achieved by mixing GO prepared by modified Hummer's method (GO-H) with proper particle size of Mn2O3 (241 nm). The mechanism of this sensor is based on the formation of Mn-O and Cr-O on the modified electrode with assistance of oxygen moieties provided by both Mn2O3 NPs and GO. The analytical performances were investigated by measuring electrochemical signal of Mn(II) and Cr(VI) by using square-wave cathodic stripping voltammetry (SWCSV). This sensor holds low electrode-to-electrode variation (relative standard deviation (RSD) < 4%) with a good limit of detection (LOD) at about 6.67 and 11.20 μg⋅L-1 for Mn(II) and Cr(VI), respectively. Applicability of this sensor was demonstrated by measuring Mn(II) and Cr(VI) in tap water samples with recovery of 90.77-103.45% and 82.34-103.73% for Mn(II) and Cr(VI) determinations, respectively. With the contribution of both GO and Mn2O3 as electrocatalysts, this developed sensor iscapable to be usedfor water quality monitoring in real samples.

Elimination of experimentation cost and time by data analysis in mechanical property prediction of aluminum alloys

Discovery of new materials in material science involves lot of processes like experimental trials and laboratory testing which consumes lot of time and energy. This process requires theoretical foundation to avoid errors and also constrained by laboratory environment conditions. To overcome the above issues, data analysis techniques are used for material property prediction for the new materials which reduces lot of time and energy by avoiding experimentation. Plenty of data is available in the material science field for the analysis. In this work, Aluminum alloy composite properties are collected and analysed for the mechanical property prediction. Data available in this case is the labelled data and the output is a continuous variable, so the supervised regression algorithms such as linear regression (LR), K- Nearest Neighbor algorithm (KNN) and Artificial Neural Network (ANN) are proposed for the tensile strength prediction of aluminum alloys. The performance of LR, KNN and ANN are measured in terms of R2 value respectively 90.5%, 96.5% and 89%. K-Nearest Neighbor algorithm is selected as best algorithm because of its accurate prediction and the testing data is varied between 10 and 30 percentage and the R-square values are calculated for 10, 20 and 30 percentage of test size are 94.3%, 93.2% and 96.3%. Best algorithm with proper test size is found using above methodology. Similar methodology can be applied for predicting the other properties of aluminum composites and also for other composites.

The application of fuzzy logic techniques to improve decision making in apparel size

Traditional set theory, or crisp set theory, is built on the concept of crisp sets. These are sets for which the membership of an element within a set is defined as either true or false; in or out; 1 or 0. This construction is extremely useful, as mathematics has shown, but it struggles to model concepts of our world that possess vagueness or uncertainty. Therefore, we explore an expansion of set theory to allow an element to be partially within a set, thus constituting what is known as a fuzzy set. This paper introduces the basic concept of fuzzy sets, which includes fuzzy sets and crisp sets, as well as the operations of a fuzzy set and fuzzy classification systems. Fuzzy logic has been utilized to solve numerous textile-related difficulties, one of which was determining the proper clothing size. In this study, we examined fuzzy logic applications in textiles, such as the construction of fuzzy expert systems and fuzzy logic for predicting clothing size. This research demonstrates that when determining the correct size of clothing, the outcome is heavily reliant on fuzzy logic.

Verifying a dominant cause of output variation

Finding the dominant cause(s) of variation in process improvement projects is an important task. Before trying to reduce variation in the dominant cause or mitigate the effect of variation in the dominant cause to reduce output variation, it is strongly recommended that we verify we have identified the true (dominant) cause. This article is about how best to verify we have correctly identified a dominant cause, as the existing literature does not properly answer this question. Although it may seem that a randomized controlled experiment is sufficient for this purpose, we show that experimental studies alone cannot provide all the required information. An experiment identifies whether a suspect is a cause of variation; however, we also require additional information (i.e., from observational studies) to determine whether it is dominant and not just significant. This article lists some viable composite study designs, assesses their relative merits, and recommends proper sample sizes. We also investigate how to systematically conduct a verification study in the era of smart manufacturing. Moreover, we provide a tangible example to illustrate our proposed procedure.

Comparative analyses of covariance matrix adaptation and iterative ensemble smoother on high-dimensional inverse problems in high-resolution groundwater modeling

Parameter estimation is an inverse problem which is crucial to reliable groundwater model predictions and management. Numerous techniques have been developed to address this challenging problem. This study aims to compare the performance of a stochastic optimization method, covariance matrix adaptation-evolution strategy (CMA-ES), and a data assimilation method, Levenberg-Marquardt based iterative ensemble smoother (ES-LM), on solving inverse problems in groundwater modeling. This study also presents a parallelization strategy to accelerate their implementation. The comparative analysis involves synthetic inverse problems and a real-world case study of calibrating a high-resolution groundwater model. Results from the synthetic problems suggest that both the CMA-ES and the ES-LM are able to achieve desirable data fitting, but the ES-LM generally exhibits greater efficiency than the CMA-ES. Also, with appropriate stopping criteria, both methods can perform well on propagating uncertainty of model predictions. However, both methods can underestimate the uncertainty of model parameters derived from Bayesian posterior. The underestimation is particularly pronounced by the ES-LM when using a small ensemble size, whereas uncertainty quantification by the CMA-ES is less affected by population size. Moreover, calibration of the highly parameterized groundwater model in Louisiana and Southwest Mississippi indicates that the ES-LM can outperform the CMA-ES for high-dimensional inverse problems regarding both data match and computational costs. With proper ensemble size, the two methods can produce comparable uncertainty of model parameters and predictions. This study contributes to the scientific understanding and efficient application of the CMA-ES and the ES-LM.

Optimal sizing design of the hydrogen-based system for the stand-alone island

The main barriers to using Renewable Energy Sources (RES) to generate electricity are intermittency with low penetration in the electricity network and high seasonal demand fluctuation. An optimum hydrogen-based system configuration is designed for absorbing the maximization of RES curtailments using water electrolysis to produce and store hydrogen. This paper also developed a simulation model to optimize the proper size of electrolyzers and other parameters of the whole system. In order to validate the model, a hybrid electricity network for relatively stand-alone islands, located in eastern coastal China, has been used as a case study. Our analysis showed that the sizing of the required electrolyzer was determined by the operational temperature, the cell U-I curves, and the input of usable RES curtailments. For the application, a large amount of available hydrogen could be produced from the RES curtailments with an average conversion efficiency higher than 75.4%. And almost half of the rejected RES energy (54.7%) was returned to the electrical network through fuel cell stacks during high demand periods.

A compact Ka‐band MMIC power amplifier using continuous Class‐B/J mode technique

AbstractThis letter presents a Ka‐band GaAs broadband high efficiency power amplifier (PA). The power amplifier achieves the best trade‐off among gain, output power, power added efficiency (PAE), and fractional bandwidth through nonlinear simulations to select proper device size and bias conditions. The high‐efficiency and broadband performances are achieved by using continuous Class‐B/J mode for fundamental and second harmonics output matching network. The fabricated PA achieves a power gain of 18.8 dB, a 3‐dB bandwidth of 25–30.2 GHz, a saturation power 25.5 dBm, a peak PAE of 30.0%. The chip size is as compact as 1.08 mm2 including all pads.

State-of-the-art review on WWTPs in Antioquia: levels, technologies, flaws and operational setbacks

The number of wastewater treatment plants (WWTPs) in Antioquia (Colombia) increased 74 % over the last nine years. This increase brought to light we need for a unified guide to prevent flaws in the future WWTPs. Therefore we set out to design a guide to meet this new need. To achieve this design we had four main stages. First, we chose the 89 WWTPs from previous experience that we had in the operational stage. Second, we classified these WWTPs into levels and technologies. Third, we described various flaws in these WWTPs. Fourth, we designed a guide with these WWTPs technologies mostly used in Antioquia. Here we described that these WWTPs mostly used a combination of various levels and technologies. These levels were pre-treatment, primary treatment, secondary treatment, and sludge treatment. These technologies were primary sedimentation basins (with inclined plates), UASB reactors, anaerobic packed-bed reactors, anaerobic sludge digesters, and conventional sands beds. These WWTPs mostly served a population of less than 30.000. And these WWTPs had various flaws and operational setbacks. These flaws were the inappropriate localization of these WWTPs, low retention times in these anaerobic packed-bed reactors and the lack of proper size in these conventional sands beds. These setbacks were the unequal distribution 174 REVISTA PRODUCCIÓN + LIMPIA–Vol. 18 No 1 – enero/junio – 2023 / S. Londoño State-of-the-art review on WWTPs in Antioquia: levels, technologies, flaws and operational setbacks of influent and effluent collection in these UASB reactors and anaerobic reactors. We suggest that we could use this guide in 2 different ways. First, to prevent these flaws in the future WWTPs. Second, to improve the training guides for operators in charge of these WWTPs, which makes the operations more efficient.

Analysis of hydrogen infrastructure for the feasibility, economics, and sustainability of a fuel cell powered data center

Data centers used for internet data services, cloud computing, and/or data storage consume vast amounts of electricity and are increasing rapidly in capacity. Consequently, their power consumption has raised concerns about energy sustainability and environmental impacts. Large-scale, on-site renewable energy could help reduce data centers’ carbon footprint; however, wind and solar power alone cannot provide an uninterrupted power supply to computer servers due to their natural variability. Instead, reliable power integration can be achieved by using fuel cells powered by hydrogen from sustainable resources (e.g., wind and solar energy). Establishing a hydrogen infrastructure will be critical for realizing these benefits and establishing fuel cells as a viable power source for data centers. To facilitate the development of novel carbon-free fuel cell data enters, this paper presents renewable power integrated with hydrogen infrastructures in four scenarios to provide reliable hydrogen supply from production to storage. Various paths were analyzed toward a hydrogen supply infrastructure by determining the proper component sizes and calculating the cost of meeting the server load. We used a microgrid modeling software, Hybrid Optimization of Multiple Energy Resources (HOMER), and studied the feasibility of fuel cell powered data centers employing renewable hydrogen. The modeling results show various renewable integration configurations to meet reliable and sustainable power requirement under four scenarios for a carbon-free data center.

Small-Volume Microstrip Patch Antennas Exactly Covering Wi-Fi 6 Bands of 2.4–2.5 GHz and 5.15–5.85 GHz

The sixth generation of wireless fidelity (Wi-Fi 6) spectrums possess two unbalanced bands of 2.4~2.5 GHz (4.1%) and 5.15~5.85 GHz (12.7%). Most of the existing dual-band Wi-Fi 6 patch antennas are either with a superfluous lower or an insufficient higher bandwidth. It remains a challenge to cover these two bands with an exact way. This paper addresses the issue of the exact coverage problem of the unbalanced Wi-Fi 6 bands by engineering the TM0.5,0 and TM0.5,1 modes of the half-mode patch while maintaining a very small volume. The design guideline follows three steps: first, select proper patch size to make the first two modes resonate at 2.45 GHz and 5.5 GHz using two ports. Second, optimize feeding positions to make both impedance trajectories in Smith Chart with minimum sizes. Third, use lumped components to make the two resonances with good matching condition. Using the novel matching method, the resultant half-mode patch with a profile of 3 mm (0.024λL/0.052λH) and size of 22.55×23.95 mm2 (0.18×0.19λL2/0.39×0.41λH2) exactly covers the Wi-Fi 6 bands, indicating that the antenna space is taken up fully. As far as authors know, the proposed antenna is with the smallest volume compared to all the reported Wi-Fi 6 dual-band patch antennas. Moreover, the proposed antenna is with dual port and aperture sharing properties, which suits the optimal Wi-Fi 6 radio frequency front-end architecture.

Early Doffing among Frontline Healthcare Workers Working in the COVID Areas in Resource-Limited Settings: Lessons Learned and Interventions Taken.

In the fight against the pandemic, personal protective equipment (PPE) has played a vital role. However, working with PPE has its own difficulties and problems. The aim of this study was to find out the reasons of early doffing, that is, in doffing in emergency situations before the shift among healthcare workers (HCWs) working in PPE in the COVID areas in resource-limited settings. This cross-sectional, survey-based study was carried out on healthcare workers involved in direct care of patients with COVID-19 in tertiary care COVID center, India. The questionnaire was sent as a Google form through email and social media platforms like WhatsApp and Facebook. The data was reported as the mean ± SD for continuous variables and as the percentage for categorical variables. Among 252 healthcare workers who participated in the survey, 145 (57.5%) participants doffed early on 300 occasions. Out of these 145, 50% doffed early only once and rest doffed early multiple times. The most common reason of early doffing was found to be breach in PPE (15.33%) followed by fogging (14%) and headache (12%), and most commonly, breach was in mask or coverall/gown (32.6% each). Although PPE decreases the risk of infection, it is challenging for HCWs to work in PPE leading to instances of early doffing. The most common reason for early doffing in our study was the breach in PPE and the most common component of breach was found to be mask and coverall/gown. Therefore, we suggest that the proper sizes of the PPE should be made available and proper training in donning should be imparted to HCWs working in PPE.

Highly effective graphene membrane for transformer oil purification

A highly effective adsorbent comprised of the Graphene membrane was created using the sonification method in this study. For all we know, the resulting Graphene membrane was employed as an adsorbent for transformer oil purification for the first time. The quality of the treated oil was evaluated through several factors, such as acid number TAN(mg KOH/g), color, breakdown voltage, and viscosity. The results indicated that the Graphene membrane exceeded the other commercial adsorbents in terms of purifying capacity. Several reasons, including the unreduced oxygen functional groups, vacancy defects on the surface of the Graphene flake, the high specific surface area, and the proper pore size of the Graphene membrane for oil refining, might explain this exceptional outcome. Furthermore, the 3D structure of the Graphene membrane can extend the residence purification time of the transformer oil, assisting in the improvement of the purification capability of the Graphene membrane.

Machine Learning in Environmental Research: Common Pitfalls and Best Practices.

Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications.

The 14 Her Planetary System: Companion Masses and Architecture from Radial Velocities and Astrometry

We combine Hubble Space Telescope (HST) Fine Guidance Sensor, Hipparcos, and Gaia DR3 astrometric observations of the K0 V star 14 Her with the results of an analysis of extensive ground-based radial velocity data to determine perturbation orbits and masses for two previously known companions, 14 Her b and c. Radial velocities obtained with the Hobby–Eberly Telescope and from the literature now span over 25 years. With these data we obtain improved radial velocity (RV) orbital elements for both the inner companion, 14 Her b and the long-period outer companion, 14 Her c. We also find evidence of an additional RV signal with P ∼ 3789d. We then model astrometry from Hipparcos, HST, and Gaia with RV results to obtain system parallax and proper motion, perturbation periods, inclinations, and sizes due to 14 Her b and c. We find P b = 1767.6 ± 0.2 days, perturbation semimajor axis α b = 1.3 ± 0.1 mas, and inclination i b = 36° ± 3°, P c = 52160 ± 1028 days, perturbation semimajor axis α c = 10.3 ± 0.7 mas, and inclination i c = 82° ± 14°. In agreement with a past investigation, the 14 Her b, c orbits exhibit significant mutual inclination. Assuming a primary mass M * = 0.98 ± 0.04M ☉, we obtain companion masses and .

EffConv: Efficient Learning of Kernel Sizes for Convolution Layers of CNNs

Determining kernel sizes of a CNN model is a crucial and non-trivial design choice and significantly impacts its performance. The majority of kernel size design methods rely on complex heuristic tricks or leverage neural architecture search that requires extreme computational resources. Thus, learning kernel sizes, using methods such as modeling kernels as a combination of basis functions, jointly with the model weights has been proposed as a workaround. However, previous methods cannot achieve satisfactory results or are inefficient for large-scale datasets. To fill this gap, we design a novel efficient kernel size learning method in which a size predictor model learns to predict optimal kernel sizes for a classifier given a desired number of parameters. It does so in collaboration with a kernel predictor model that predicts the weights of the kernels - given kernel sizes predicted by the size predictor - to minimize the training objective, and both models are trained end-to-end. Our method only needs a small fraction of the training epochs of the original CNN to train these two models and find proper kernel sizes for it. Thus, it offers an efficient and effective solution for the kernel size learning problem. Our extensive experiments on MNIST, CIFAR-10, STL-10, and ImageNet-32 demonstrate that our method can achieve the best training time vs. accuracy trade-off compared to previous kernel size learning methods and significantly outperform them on challenging datasets such as STL-10 and ImageNet-32. Our implementations are available at https://github.com/Alii-Ganjj/EffConv.

A fine balance among key biophysical factors is required for recovery of bipolar mitotic spindle from monopolar and multipolar abnormalities.

During mitosis, equal partitioning of chromosomes into two daughter cells requires assembly of a bipolar mitotic spindle. Because the spindle poles are each organized by a centrosome in animal cells, centrosome defects can lead to monopolar or multipolar spindles. However, the cell can effectively recover the bipolar spindle by separating the centrosomes in monopolar spindles and clustering them in multipolar spindles. To interrogate how a cell can separate and cluster centrosomes as needed to form a bipolar spindle, we developed a biophysical model, based on experimental data, which uses effective potential energies to describe key mechanical forces driving centrosome movements during spindle assembly. Our model identified general biophysical factors crucial for robust bipolarization of spindles that start as monopolar or multipolar. These factors include appropriate force fluctuation between centrosomes, balance between repulsive and attractive forces between centrosomes, exclusion of the centrosomes from the cell center, proper cell size and geometry, and a limited centrosome number. Consistently, we found experimentally that bipolar centrosome clustering is promoted as mitotic cell aspect ratio and volume decrease in tetraploid cancer cells. Our model provides mechanistic explanations for many more experimental phenomena and a useful theoretical framework for future studies of spindle assembly.

PME: pruning-based multi-size embedding for recommender systems.

Embedding is widely used in recommendation models to learn feature representations. However, the traditional embedding technique that assigns a fixed size to all categorical features may be suboptimal due to the following reasons. In recommendation domain, the majority of categorical features' embeddings can be trained with less capacity without impacting model performance, thereby storing embeddings with equal length may incur unnecessary memory usage. Existing work that tries to allocate customized sizes for each feature usually either simply scales the embedding size with feature's popularity or formulates this size allocation problem as an architecture selection problem. Unfortunately, most of these methods either have large performance drop or incur significant extra time cost for searching proper embedding sizes. In this article, instead of formulating the size allocation problem as an architecture selection problem, we approach the problem from a pruning perspective and propose Pruning-based Multi-size Embedding (PME) framework. During the search phase, we prune the dimensions that have the least impact on model performance in the embedding to reduce its capacity. Then, we show that the customized size of each token can be obtained by transferring the capacity of its pruned embedding with significant less search cost. Experimental results validate that PME can efficiently find proper sizes and hence achieve strong performance while significantly reducing the number of parameters in the embedding layer.

Mesopore engineering of ZIF-8 by [Bmim][Tf2N] positioning into nanocage for enhanced CO2 capture

The regulation on cage size and CO2-philic environment of metal–organic frameworks (MOFs) is essential to improve CO2 capture. In this work, [Bmim][Tf2N] was proposed to position into cages of ZIF-8 to achieve the selective permeation of CO2 by pore screening and CO2 adsorption. [Bmim][Tf2N] was introduced into cages of ZIF-8 by “adsorption-infiltration” strategy. Due to the strong interaction between [Tf2N]− and ZIF-8, the cages present a CO2-philic environment, which preferentially attracts CO2 to enter these cages, ensuring the dissolution selectivity of CO2. Besides, the proper cage size tuned by [Bmim][Tf2N] provides CO2 with a faster transport channel due to its smaller average kinetic diameter than N2, guaranteeing the diffusion selectivity of CO2. Consequently, the CO2 separation in [Bmim][Tf2N]@ZIF-8/Pebax MMMs is intensified. With increasing the [Bmim][Tf2N] content in cages of ZIF-8, the pore volume by BET results decreases and the CO2 adsorption from molecular simulation increases. In response, the CO2/N2 selectivity of [Bmim][Tf2N]@ZIF-8/Pebax MMMs is enhanced constantly. Meanwhile, the CO2 permeability is declined due to the narrowed cage size. All of these results demonstrate the significance of pore screening and CO2 adsorption induced by [Bmim][Tf2N]. Specifically, [Bmim][Tf2N]8@ZIF-8/Pebax MMM at filler loading of 15 wt% exhibits the CO2 permeability of 108.3 Barrer and the CO2/N2 selectivity of 86.7, exceeding the 2008 Robeson upper bound. Especially the CO2/N2 selectivity locates at a higher level than those of most reported works, indicating that appropriate tuning of the physicochemical properties within the nanocages of MOFs is significative to improve CO2 capture.

A note of caution on numerical scheme selection: Evidence from cyclone separator CFD simulations with appropriate near-wall grid sizes

In this paper two things are done. (i) The appropriate numerical scheme set for computational fluid dynamics (CFD) simulations of cyclone separators with various vortex-finder-to-cone-tip diameter ratios (D⁎) has been achieved by comparing the mean flow patterns and performances simulated by two previously proposed numerical scheme sets. The predicted results revealed that the CFD simulations of different cyclone separator designs require the third-order accuracy scheme and proper near-wall grid sizes to preserve flow similarity, especially for cyclone separators with D⁎ < 1. Therefore, the present research alerts caution concerning the lower-order numerical scheme for cyclone separator CFD simulation. (ii) The present CFD work confirms that the near-wall grid size estimation method originally developed for gas cyclones is a possible method for estimating near-wall grid sizes for hydrocyclone with air core diameter (Da) ≤ 0.4Dv. In addition, the capabilities of pressure-strain sub-models for cyclone separator CFD simulations were preliminarily discussed.

Pleural Drain Insertion: Surgical Technique, Aftercare, and Removal

Pleural drain insertion is a commonly employed technique used for several indications, including the drainage of haemothorax and pneumothorax. Difficulties can be encountered without proper techniques, especially in obese patients. We describe and illustrate our stepwise approach used during chest drain insertion. The points emphasised include: appropriate use of sedation, use of local anaesthetics, pleural anaesthesia, making a deep incision in the intercostal space, using a proper size and type of haemostat to enter the pleura and dilate the tract, blunt dissection with a finger, using the haemostat to ensure basal or apical placement of the drain, fixation of the drain with a vertical mattress suture to secure the tube, and placing the drain on suction for effective evacuation of the pleural cavity. Insertion of a “surgical” drain requires special techniques and precautions to ensure safe and effective drainage of the pleural cavity. Ultrasound may also be utilised to aid drain placement. Understanding the “bubble and swing” after insertion is also explained in simple terms to guide aftercare of the drain and indications for removal. A description of a “clamp test” to guide the decision for removal, if required, is included. The evidence for optimal timing of removal in the respiratory cycle is also reviewed and explained in a stepwise approach with practical tips for patient counselling.